import cv2 as cv
import librosa
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sounddevice as sd
import time as time1

from ipywidgets import interact, widgets
from matplotlib import pyplot as plt
from matplotlib.widgets import Slider, Button, TextBox
from scipy.io.wavfile import write

jellyfish = cv.imread('img/jellyfish.jpg')
print(f"{jellyfish.shape=}\n{jellyfish.dtype=}\n{np.max(jellyfish)=}\n{np.min(jellyfish)=}")
jellyfish = cv.cvtColor(jellyfish, cv.COLOR_BGR2RGB)

plt.imshow(jellyfish)
plt.show()

cavediving = cv.imread('img/cavediving.jpg')
print(f"{cavediving.shape=}\n{cavediving.dtype=}\n{np.max(cavediving)=}\n{np.min(cavediving)=}")
cavediving = cv.cvtColor(cavediving, cv.COLOR_BGR2RGB)

plt.imshow(cavediving)
plt.show()

sunrise = cv.imread('img/sunrise.jpg')
print(f"{sunrise.shape=}\n{sunrise.dtype=}\n{np.max(sunrise)=}\n{np.min(sunrise)=}")
sunrise = cv.cvtColor(sunrise, cv.COLOR_BGR2RGB)
plt.imshow(sunrise)
plt.show()

jellyfish.shape=(3648, 5472, 3)
jellyfish.dtype=dtype('uint8')
np.max(jellyfish)=np.uint8(255)
np.min(jellyfish)=np.uint8(0)

cavediving.shape=(3648, 5472, 3)
cavediving.dtype=dtype('uint8')
np.max(cavediving)=np.uint8(255)
np.min(cavediving)=np.uint8(0)

sunrise.shape=(4000, 6000, 3)
sunrise.dtype=dtype('uint8')
np.max(sunrise)=np.uint8(255)
np.min(sunrise)=np.uint8(0)

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt

# Function to adjust brightness for an RGB image
def adjust_brightness(image, brightness=0):
    """
    Adjust the brightness of an RGB image.
    
    Parameters:
    - image: Input image in RGB format
    - brightness: Amount to adjust brightness (can be positive or negative)
    
    Returns:
    - Brightness-adjusted image
    """
    image = np.int16(image)
    image = image + brightness
    image = np.clip(image, 0, 255)  # Ensure values stay within [0, 255]
    return np.uint8(image)

def adjust_hue(image, hue_shift=0):
    """
    Adjust the hue of an RGB image.
    
    Parameters:
    - image: Input image in RGB format
    - hue_shift: Amount to shift the hue by (in degrees)
    
    Returns:
    - Hue-adjusted image
    """
    hsv_image = cv.cvtColor(image, cv.COLOR_RGB2HSV)
    hue = hsv_image[:, :, 0].astype(int)
    hue = (hue + hue_shift) % 180  # Ensure hue stays within the range [0, 179]
    hsv_image[:, :, 0] = hue.astype(np.uint8)
    return cv.cvtColor(hsv_image, cv.COLOR_HSV2RGB)

def apply_adjustments(image, brightness=0, hue_shift=0):
    """
    Apply brightness and hue adjustments to an RGB image.
    
    Parameters:
    - image: Input image in RGB format
    - brightness: Amount to adjust brightness
    - hue_shift: Amount to shift the hue by

    Returns:
    - Image with brightness and hue adjustments applied
    """
    bright_image = adjust_brightness(image, brightness)
    final_image = adjust_hue(bright_image, hue_shift)
    return final_image

# Function to display the original and adjusted images side by side
def display_images_side_by_side(image, brightness=0, hue_shift=0, ax=None, title="Image"):
    """
    Display the original and adjusted images side by side for comparison.
    
    Parameters:
    - image: The input image in RGB format
    - brightness: Amount to adjust brightness
    - hue_shift: Amount to shift the hue by
    """
    adjusted_image = apply_adjustments(image, brightness, hue_shift)
    
    # Original image
    ax[0].imshow(image)
    ax[0].set_title(f'Original {title}')
    ax[0].axis('off')
    
    # Adjusted image
    ax[1].imshow(adjusted_image)
    ax[1].set_title(f'Adjusted {title} (Brightness: {brightness}, Hue: {hue_shift})')
    ax[1].axis('off')

def display_all_images():
    """
    Display all images with fixed adjustments for brightness and hue.
    """
    # Fixed values for brightness and hue for each image
    brightness_jellyfish = -40
    hue_jellyfish = 40
    
    brightness_cave = -10
    hue_cave = 20
    
    brightness_sunrise = 10
    hue_sunrise = -20

    fig, axes = plt.subplots(3, 2, figsize=(12, 12))

    display_images_side_by_side(jellyfish, brightness_jellyfish, hue_jellyfish, axes[0], "jellyfish")
    display_images_side_by_side(cavediving, brightness_cave, hue_cave, axes[1], "cave diving")
    display_images_side_by_side(sunrise, brightness_sunrise, hue_sunrise, axes[2], "sunrise")
    
    plt.tight_layout()
    plt.savefig("img/adjusted_images2x3.png", dpi=300)
    plt.show()

display_all_images()

def adjust_gamma(image, gamma=1.0):
    """
    Adjust the gamma correction of an image.

    Parameters:
    - image: Input image in RGB format
    - gamma: Gamma value for correction (default is 1.0)
    
    Returns:
    - Gamma-corrected image
    """
    inv_gamma = 1.0 / gamma
    table = np.array([(i / 255.0) ** inv_gamma * 255 for i in np.arange(0, 256)]).astype("uint8")
    return cv.LUT(image, table)

def apply_region_specific_brightness(image, brightness_value):
    """
    Apply region-specific brightness adjustment to an image.

    Parameters:
    - image: Input image in RGB format
    - brightness_value: Amount to adjust brightness (can be positive or negative)
    
    Returns:
    - Image with region-specific brightness adjustment applied
    """
    mask = image[:, :, 2] < 100  # Apply brightness adjustment to the darker regions only
    brightened_image = adjust_brightness(image.copy(), brightness_value)
    image[mask] = brightened_image[mask]
    return image

def apply_clahe(image, clip_limit=2.0, tile_grid_size=(8, 8)):
    """
    Apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to an image.
    
    Parameters:
    - image: Input image in RGB format
    - clip_limit: Threshold for contrast limiting
    - tile_grid_size: Size of grid for histogram equalization

    Returns:
    - Image with CLAHE applied
    """
    lab_image = cv.cvtColor(image, cv.COLOR_RGB2LAB)
    clahe = cv.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size)
    lab_image[:, :, 0] = clahe.apply(lab_image[:, :, 0])
    return cv.cvtColor(lab_image, cv.COLOR_LAB2RGB)


def apply_vignette(image, radius_factor=2, brightness_reduction=0.5):
    """
    Apply a vignette effect to an image.

    Parameters:
    - image: Input image in RGB format
    - radius_factor: Factor to control the size of the vignette
    - brightness_reduction: Amount to reduce brightness at the edges

    Returns:
    - Image with vignette effect applied
    """
    rows, cols = image.shape[:2]
    center_x, center_y = cols // 2, rows // 2
    # Create a Gaussian kernel that will serve as the vignette mask
    # Calculate the maximum possible radius
    max_radius = np.sqrt(center_x ** 2 + center_y ** 2) / radius_factor
    # Create meshgrid for the X and Y coordinates
    x = np.arange(cols)
    y = np.arange(rows)
    X, Y = np.meshgrid(x, y)
    # Calculate the distance from the center of the image
    distance_from_center = np.sqrt((X - center_x) ** 2 + (Y - center_y) ** 2)
    # Normalize the distances to a range between 0 and 1, then reverse (1 at center, 0 at edges)
    vignette_mask = np.clip(1 - (distance_from_center / max_radius), 0, 1)
    # Apply brightness reduction (e.g., 0.5 means reduce brightness by 50% at the edges)
    vignette_mask = vignette_mask ** brightness_reduction  # Adjust the strength of reduction
    # Apply the vignette mask to each channel (RGB)
    vignette_mask_3d = vignette_mask[:, :, np.newaxis]  # Make mask compatible with 3-channel image
    vignette_image = np.uint8(image * vignette_mask_3d)
    return vignette_image


# Experiment: Create three levels of brightness adjustment (80%, 60%, 40%)
brightness_levels = [-15, -30, -45]
gamma_values = [0.85, 0.7, 0.55]
vignette_values = [0.85, 0.7, 0.55]
clahe_clip_limits = [1.0, 2.0, 3.0]

# Create a 3x3 grid for each method and level of adjustment
fig, ax = plt.subplots(5, 3, figsize=(15, 13))

# Row 1: Linear brightness adjustment
for i, brightness in enumerate(brightness_levels):
    adjusted_image = adjust_brightness(jellyfish.copy(), brightness)
    ax[0, i].imshow(adjusted_image)
    ax[0, i].set_title(f'Linear Brightness: {100 + brightness}%')
    ax[0, i].axis('off')

# Row 3: Region-specific brightness adjustment
for i, brightness in enumerate(brightness_levels):
    region_adjusted_image = apply_region_specific_brightness(jellyfish.copy(), brightness)
    ax[1, i].imshow(region_adjusted_image)
    ax[1, i].set_title(f'Region-Specific: {100 + brightness}%')
    ax[1, i].axis('off')
    
# Row 2: Gamma correction
for i, gamma in enumerate(gamma_values):
    gamma_corrected_image = adjust_gamma(jellyfish.copy(), gamma)
    ax[2, i].imshow(gamma_corrected_image)
    ax[2, i].set_title(f'Gamma Correction: {gamma}')
    ax[2, i].axis('off')

# Row 4: CLAHE with different clip limits (strengths)
for i, clip_limit in enumerate(clahe_clip_limits):
    clahe_image = apply_clahe(jellyfish.copy(), clip_limit=clip_limit)
    ax[3, i].imshow(clahe_image)
    ax[3, i].set_title(f'CLAHE: Clip Limit = {clip_limit}')
    ax[3, i].axis('off')

# Row 1: Linear brightness adjustment
for i, brightness in enumerate(vignette_values):
    adjusted_image = apply_vignette(jellyfish.copy(), 1, brightness)
    ax[4, i].imshow(adjusted_image)
    ax[4, i].set_title(f'vignette Correction: {brightness}')
    ax[4, i].axis('off')


plt.tight_layout()
plt.savefig("img/brightnessexperiment.png", dpi=300)
plt.show()

dark_adjusted_jellyfish = adjust_gamma(jellyfish.copy(), 0.55)

# Display the original and adjusted images side by side
fig, ax = plt.subplots(1, 2, figsize=(12, 6))
ax[0].imshow(jellyfish)
ax[0].set_title('Original Jellyfish')
ax[0].axis('off')

ax[1].imshow(dark_adjusted_jellyfish)
ax[1].set_title('Adjusted Jellyfish: Gamma = 0.55')
ax[1].axis('off')
cv.imwrite("img/dark_adjusted_jellyfish.png", cv.cvtColor(dark_adjusted_jellyfish, cv.COLOR_RGB2BGR))
plt.tight_layout()
plt.savefig("img/dark_adjusted_jellyfish_comparison.png", dpi=300)
plt.show()

hue_shifts = [-80, -60, -40, -20, 0, 20, 40, 60, 80]

# Create a 3x3 grid for hue adjustments
fig, ax = plt.subplots(3, 3, figsize=(12, 12))

for i, hue_shift in enumerate(hue_shifts):
    hue_adjusted_image = adjust_hue(dark_adjusted_jellyfish.copy(), hue_shift=hue_shift)
    
    row = i // 3
    col = i % 3
    
    ax[row, col].imshow(hue_adjusted_image)
    ax[row, col].set_title(f'Hue Shift: {hue_shift}°')
    ax[row, col].axis('off')

plt.tight_layout()
plt.savefig("img/hue_experiment.png", dpi=300)
plt.show()

# Apply hue shifts to different quadrants of the image
def apply_quadrant_hue_shifts(image, hue_shifts):
    """
    Apply different hue shifts to each quadrant of the image.

    Parameters:
    - image: Input image in RGB format
    - hue_shifts: List of hue shifts for each quadrant (in degrees)
    """
    # Define the quadrants
    #jellyfish.shape is (3648, 5472, 3)
    rowsplit = 2000
    columnsplit = 3150

    top_left = image[0:rowsplit, 0:columnsplit]
    top_right = image[0:rowsplit, columnsplit:]
    bottom_right = image[rowsplit:, columnsplit:]
    bottom_left = image[rowsplit:, 0:columnsplit]
    
    # Apply hue shifts to each quadrant
    top_left = adjust_hue(top_left, hue_shifts[0])
    top_right = adjust_hue(top_right, hue_shifts[1])
    bottom_right = adjust_hue(bottom_right, hue_shifts[2])
    bottom_left = adjust_hue(bottom_left, hue_shifts[3])
    
    # Reconstruct the full image with the adjusted quadrants
    top_half = np.hstack((top_left, top_right))
    bottom_half = np.hstack((bottom_left, bottom_right))
    adjusted_image = np.vstack((top_half, bottom_half))
    filename = f"img/quadrant_hue_shifts{hue_shifts[0]}_{hue_shifts[1]}_{hue_shifts[2]}_{hue_shifts[3]}.png"
    cv.imwrite(filename, cv.cvtColor(adjusted_image, cv.COLOR_RGB2BGR))
    print(f"Image saved as {filename}")

apply_quadrant_hue_shifts(dark_adjusted_jellyfish.copy(), [0, 40, 80, 120])
apply_quadrant_hue_shifts(dark_adjusted_jellyfish.copy(), [20, 60, 100, 140])

Image saved as img/quadrant_hue_shifts0_40_80_120.png
Image saved as img/quadrant_hue_shifts20_60_100_140.png

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt

# Function to apply smooth sliding hue shift from 0° to 180° across the image
def apply_smooth_sliding_hue_shift(image):
    """
    Apply a smooth sliding hue shift from 0° to 180° across the image, left to right.
    
    Parameters:
    - image: Input RGB image.
    
    Returns:
    - Image with a smooth sliding hue shift applied from left to right.
    """
    hsv_image = cv.cvtColor(image, cv.COLOR_RGB2HSV)
    cols = image.shape[1]
    
    # Create a sliding hue mask based on the column position
    for col in range(cols):
        # Compute the hue shift smoothly as a proportion of the column position
        hue_shift = int((col / cols) * 90)  # Scale hue shift between 0 and 90
        hsv_image[:, col, 0] = (hsv_image[:, col, 0] + hue_shift) % 180  # Apply the hue shift to the column
    # Convert back to RGB
    sliding_hue_image = cv.cvtColor(hsv_image, cv.COLOR_HSV2RGB)
    
    return sliding_hue_image

smooth_sliding_hue_image = apply_smooth_sliding_hue_shift(dark_adjusted_jellyfish.copy())
cv.imwrite("img/smooth_sliding_hue_shift_0_to_90.png", cv.cvtColor(smooth_sliding_hue_image, cv.COLOR_RGB2BGR))

True

# Function to resize image to a smaller resolution using Lanczos interpolation
def resize_image(image, width=1000):
    """
    Resize the image while maintaining the aspect ratio, using Lanczos interpolation for better color quality.
    
    Parameters:
    - image: Input image in RGB format
    - width: The desired width of the output image
    
    Returns:
    - Resized image with improved interpolation
    """
    height = int(image.shape[0] * (width / image.shape[1]))  # Maintain aspect ratio
    # Use Lanczos interpolation for better quality when resizing
    return cv.resize(image, (width, height), interpolation=cv.INTER_LANCZOS4)

# Resize the image using the updated function
resized_image = resize_image(dark_adjusted_jellyfish.copy(), width=1000)


# Create frames for the GIF
def generate_hue_shift_frames(image, output_folder, num_frames=30):
    """
    Generate frames for a smooth hue shift animation.

    Parameters:
    - image: Input image in RGB format
    - output_folder: Folder to save the frames
    - num_frames: Number of frames to generate

    Returns:
    - List of frame paths
    """
    step = 180 // num_frames  # Define the hue step size
    frames = []
    
    for i in range(0, 180, step):
        hue_shifted_image = adjust_hue(image.copy(), i)
        frame_path = f"{output_folder}/frame_{i}.png"
        cv.imwrite(frame_path, cv.cvtColor(hue_shifted_image, cv.COLOR_RGB2BGR))  # Save frame
        frames.append(frame_path)
    
    return frames

# Generate frames for the resized image
frames = generate_hue_shift_frames(resized_image, output_folder='hue_frames', num_frames=60)

from PIL import Image

# Create a GIF from the saved PNG frames with dithering
def create_gif(frames, output_path, duration=100):
    images = [Image.open(frame) for frame in frames]
    
    # Save the GIF with dithering enabled to help smooth color transitions
    images[0].save(
        output_path, 
        save_all=True, 
        append_images=images[1:], 
        duration=duration, 
        loop=0, 
        dither=Image.Dither.FLOYDSTEINBERG,  # Apply dithering
        optimize=False  # Disable further color optimization to avoid additional color loss
    )
    
# Create the GIF with dithering to improve color quality
create_gif(frames, output_path='img/hue_shift_animation_dithered.gif', duration=100)

from scipy.stats import skew
from skimage.measure import shannon_entropy

def adjust_brightness(image, brightness=0):
    """
    Adjust the brightness of an RGB image.

    Parameters:
    - image: Input image in RGB format
    - brightness: Amount to adjust brightness (can be positive or negative)

    Returns:
    - Brightness-adjusted image
    """
    image = np.int16(image)
    image = image + brightness
    image = np.clip(image, 0, 255)  # Ensure values stay within [0, 255]
    return np.uint8(image)

# Function to apply gamma correction
def adjust_gamma(image, gamma=1.0):
    """
    Adjust the gamma correction of an image.

    Parameters:
    - image: Input image in RGB format
    - gamma: Gamma value for correction (default is 1.0)

    Returns:
    - Gamma-corrected image
    """
    inv_gamma = 1.0 / gamma
    table = np.array([(i / 255.0) ** inv_gamma * 255 for i in np.arange(0, 256)]).astype("uint8")
    return cv.LUT(image, table)

# Function to plot histograms for brightness adjustments for multiple methods
def plot_multiple_brightness_histograms_log(original_image, methods_dict):
    """
    Plots histograms for the original image and multiple brightness adjustment methods with a logarithmic scale.
    
    Parameters:
    - original_image: The original input image.
    - methods_dict: A dictionary where keys are method names and values are the corresponding adjusted images.
    """
    # Convert original to grayscale
    original_gray = cv.cvtColor(original_image, cv.COLOR_RGB2GRAY)
    
    # Number of methods + 1 for the original image
    num_methods = len(methods_dict)
    
    plt.figure(figsize=(15, 6))
    
    # Plot original image histogram (with log scale on y-axis)
    plt.subplot(1, num_methods + 1, 1)
    plt.hist(original_gray.ravel(), bins=256, range=(0, 256), color='gray')
    plt.yscale('log')  # Set y-axis to logarithmic scale
    plt.title('Original Image (Log Scale)')
    plt.xlabel('Pixel Intensity')
    plt.ylabel('Frequency')
    
    # Plot histograms for each method with log scale
    for i, (method_name, adjusted_image) in enumerate(methods_dict.items(), 2):
        adjusted_gray = cv.cvtColor(adjusted_image, cv.COLOR_RGB2GRAY)
        plt.subplot(1, num_methods + 1, i)
        plt.hist(adjusted_gray.ravel(), bins=256, range=(0, 256), color='gray')
        plt.yscale('log')  # Set y-axis to logarithmic scale
        plt.title(f'{method_name} (Log Scale)')
        plt.xlabel('Pixel Intensity')
    
    plt.tight_layout()
    plt.savefig("brightness_comparison_histograms_log.png", dpi=300)
    plt.show()

def calculate_brightness_kpis(image):
    """
    Calculate Key Performance Indicators (KPIs) for brightness of an image.

    Parameters:
    - image: Input image in RGB format
    
    Returns:
    - Dictionary of KPIs (Mean, Std Dev, Skewness, Dynamic Range, Entropy)
    """
    gray_image = cv.cvtColor(image, cv.COLOR_RGB2GRAY)
    
    # Selected KPIs
    mean = np.mean(gray_image)
    std_dev = np.std(gray_image)
    skewness = skew(gray_image.ravel())
    dynamic_range = np.max(gray_image) - np.min(gray_image)
    entropy = shannon_entropy(gray_image)
    
    return {
        "Mean": mean,
        "Std Dev": std_dev,
        "Skewness": skewness,
        "Dynamic Range": dynamic_range,
        "Entropy": entropy
    }

# Function to calculate KPIs for multiple methods
def compare_brightness_kpis(original_image, methods_dict):
    """
    Compare Key Performance Indicators (KPIs) for brightness of an image across multiple methods.
    
    Parameters:
    - original_image: The original input image.
    - methods_dict: A dictionary where keys are method names and values are the corresponding adjusted images.

    Returns:
    - Dictionary of KPIs for the original image and each adjusted image.
    """
    print("KPIs for the Original Image:")
    original_kpis = calculate_brightness_kpis(original_image)
    for kpi_name, value in original_kpis.items():
        print(f"{kpi_name}: {value}")

    for method_name, adjusted_image in methods_dict.items():
        print(f"\nKPIs for {method_name}:")
        adjusted_kpis = calculate_brightness_kpis(adjusted_image)
        for kpi_name, value in adjusted_kpis.items():
            print(f"{kpi_name}: {value}")


# Example images: original and two different brightness-adjusted methods
original_image = jellyfish.copy()

# Apply different adjustments
gamma_fish = adjust_gamma(original_image.copy(), gamma=0.55)  # Gamma correction
cv.imwrite("img/gamma_fish.png", cv.cvtColor(gamma_fish, cv.COLOR_RGB2BGR))
dark_fish = adjust_brightness(original_image.copy(), brightness=-60)  # Linear brightness adjustment
cv.imwrite("img/dark_fish.png", cv.cvtColor(dark_fish, cv.COLOR_RGB2BGR))



# Step 1: Plot histograms for multiple methods
methods_dict = {
    "Gamma Correction (0.55)": gamma_fish,
    "Linear Brightness (-60)": dark_fish
}

plot_multiple_brightness_histograms_log(original_image, methods_dict)

# Step 2: Compare KPIs for multiple methods
compare_brightness_kpis(original_image, methods_dict)

KPIs for the Original Image:
Mean: 39.41889767163935
Std Dev: 37.269260229864
Skewness: 1.9444142675661276
Dynamic Range: 254
Entropy: 6.587779365349093

KPIs for Gamma Correction (0.55):
Mean: 15.993016981988047
Std Dev: 27.93977790955635
Skewness: 3.5483630400647086
Dynamic Range: 255
Entropy: 5.137508582224073

KPIs for Linear Brightness (-60):
Mean: 11.016358849597953
Std Dev: 23.555591835182078
Skewness: 3.527971057920704
Dynamic Range: 195
Entropy: 4.078345291811171

audio, sampling_rate = librosa.load("sound/AztecWhistle.wav")
print(f"{audio=}")
print(f"{audio.dtype=}")
print(f"{audio.shape=}")
print(f"{np.max(audio)=}")
print(f"{np.min(audio)=}")
sd.play(audio, sampling_rate)

audio=array([0.00283813, 0.00253296, 0.00274658, ..., 0.        , 0.        ,
       0.        ], dtype=float32)
audio.dtype=dtype('float32')
audio.shape=(121174,)
np.max(audio)=np.float32(0.9999695)
np.min(audio)=np.float32(-0.9477539)

time = np.arange(0, len(audio)) / sampling_rate
plt.figure(figsize=(12, 4))
plt.plot(time, audio)
plt.title("Original Aztec Death Whistle Signal")
plt.xlabel("Time (s)")
plt.ylabel("Amplitude")
plt.show()

import librosa.effects

# Function to shift the frequencies
def shift_frequencies(audio, sampling_rate, shift_in_hz, direction="up"):
    """
    Shift the frequencies of an audio signal by a specified amount.

    Parameters:
    - audio: Input audio signal
    - sampling_rate: Sampling rate of the audio signal
    - shift_in_hz: Amount to shift the frequencies by (in Hz)
    - direction: Direction of the shift ("up" or "down")

    Returns:
    - Audio signal with shifted frequencies
    """
    if direction == "up":
        shift_in_steps = 12 * np.log2((shift_in_hz + sampling_rate / 2) / (sampling_rate / 2))
    elif direction == "down":
        shift_in_steps = -12 * np.log2((shift_in_hz + sampling_rate / 2) / (sampling_rate / 2))
    
    # Apply the pitch shift
    shifted_audio = librosa.effects.pitch_shift(audio, sr=sampling_rate, n_steps=shift_in_steps)
    return shifted_audio

shifted_1000_up = shift_frequencies(audio, sampling_rate, 1000)
shifted_2000_up = shift_frequencies(audio, sampling_rate, 2000)
shifted_3000_up = shift_frequencies(audio, sampling_rate, 3000)
shifted_1000_down = shift_frequencies(audio, sampling_rate, 1000, direction="down")
shifted_2000_down = shift_frequencies(audio, sampling_rate, 2000, direction="down")
shifted_3000_down = shift_frequencies(audio, sampling_rate, 3000, direction="down")

write('sound/shifted_1000_up.wav', sampling_rate, shifted_1000_up)
write('sound/shifted_2000_up.wav', sampling_rate, shifted_2000_up)
write('sound/shifted_3000_up.wav', sampling_rate, shifted_3000_up)
write('sound/shifted_1000_down.wav', sampling_rate, shifted_1000_down)
write('sound/shifted_2000_down.wav', sampling_rate, shifted_2000_down)
write('sound/shifted_3000_down.wav', sampling_rate, shifted_3000_down)

from IPython.display import Markdown, Audio

# Function to plot a single signal and provide audio playback
def plot_signal_with_audio(audio_signal, title, sampling_rate, audio_label):
    plt.figure(figsize=(10, 4))
    plt.plot(time, audio_signal)
    plt.title(title)
    plt.xlabel("Time (s)")
    plt.ylabel("Amplitude")
    plt.tight_layout()
    plt.show()

    # Display the audio player with a label
    display(Markdown(f"**{audio_label}**"))
    display(Audio(audio_signal, rate=sampling_rate))

# Plot the original signal
plot_signal_with_audio(audio, title="Original Signal", sampling_rate=sampling_rate, audio_label="Original Audio Playback")

# Plot +1000 Hz and -1000 Hz shifted signals
plot_signal_with_audio(shifted_1000_up, title="+1000 Hz Shifted Signal", sampling_rate=sampling_rate, audio_label="+1000 Hz Shifted Audio Playback")
plot_signal_with_audio(shifted_1000_down, title="-1000 Hz Shifted Signal", sampling_rate=sampling_rate, audio_label="-1000 Hz Shifted Audio Playback")

# Plot +2000 Hz and -2000 Hz shifted signals
plot_signal_with_audio(shifted_2000_up, title="+2000 Hz Shifted Signal", sampling_rate=sampling_rate, audio_label="+2000 Hz Shifted Audio Playback")
plot_signal_with_audio(shifted_2000_down, title="-2000 Hz Shifted Signal", sampling_rate=sampling_rate, audio_label="-2000 Hz Shifted Audio Playback")

# Plot +3000 Hz and -3000 Hz shifted signals
plot_signal_with_audio(shifted_3000_up, title="+3000 Hz Shifted Signal", sampling_rate=sampling_rate, audio_label="+3000 Hz Shifted Audio Playback")
plot_signal_with_audio(shifted_3000_down, title="-3000 Hz Shifted Signal", sampling_rate=sampling_rate, audio_label="-3000 Hz Shifted Audio Playback")

from IPython.display import Markdown, Audio
from scipy.signal import butter, filtfilt

# Function to apply low-pass filter
def apply_low_pass_filter(signal, sampling_rate, cutoff_hz):
    # Design a butterworth low-pass filter
    nyquist = 0.5 * sampling_rate
    normal_cutoff = cutoff_hz / nyquist
    b, a = butter(N=5, Wn=normal_cutoff, btype='low', analog=False)
    filtered_signal = filtfilt(b, a, signal)
    return filtered_signal

# Apply low-pass filters with different cutoff frequencies
low_pass_1000 = apply_low_pass_filter(audio, sampling_rate, 1000)
low_pass_2000 = apply_low_pass_filter(audio, sampling_rate, 2000)

# Plot the original and low-pass filtered signals with audio playback
def plot_filtered_signals_with_audio():
    plt.figure(figsize=(24, 12))

    # Original signal plot
    plt.subplot(3, 1, 1)
    plt.plot(time, audio)
    plt.title("Original Signal")
    plt.xlabel("Time (s)")
    plt.ylabel("Amplitude")

    # Low-pass filtered (1000 Hz) signal plot
    plt.subplot(3, 1, 2)
    plt.plot(time, low_pass_1000)
    plt.title("Low-Pass Filtered Signal (1000 Hz)")
    plt.xlabel("Time (s)")
    plt.ylabel("Amplitude")

    # Low-pass filtered (2000 Hz) signal plot
    plt.subplot(3, 1, 3)
    plt.plot(time, low_pass_2000)
    plt.title("Low-Pass Filtered Signal (2000 Hz)")
    plt.xlabel("Time (s)")
    plt.ylabel("Amplitude")

    plt.tight_layout()
    plt.show()

    # Display audio players with labels
    display(Markdown("**Original Audio Playback**"))
    display(Audio(audio, rate=sampling_rate))

    display(Markdown("**Low-Pass 1000 Hz Filtered Audio Playback**"))
    display(Audio(low_pass_1000, rate=sampling_rate))

    display(Markdown("**Low-Pass 2000 Hz Filtered Audio Playback**"))
    display(Audio(low_pass_2000, rate=sampling_rate))

# Call the function to plot and display audio
plot_filtered_signals_with_audio()

# Save the filtered audio to files
from scipy.io.wavfile import write
write('sound/low_pass_1000.wav', sampling_rate, low_pass_1000.astype(np.float32))
write('sound/low_pass_2000.wav', sampling_rate, low_pass_2000.astype(np.float32))

sd.play(low_pass_1000, sampling_rate)

sd.play(audio, sampling_rate)

sd.play(low_pass_2000, sampling_rate)

from scipy.signal import savgol_filter

# Apply Savitzky-Golay filter
def apply_savitzky_golay_filter(signal, window_length=101, polyorder=2):
    return savgol_filter(signal, window_length=window_length, polyorder=polyorder)

# Apply Savitzky-Golay filter
savitzky_golay_smoothed = apply_savitzky_golay_filter(audio)

# Plot the original and Savitzky-Golay smoothed signals
plt.figure(figsize=(12, 6))
plt.subplot(2, 1, 1)
plt.plot(time, audio)
plt.title("Original Signal")
plt.xlabel("Time (s)")
plt.ylabel("Amplitude")

plt.subplot(2, 1, 2)
plt.plot(time, savitzky_golay_smoothed)
plt.title("Savitzky-Golay Smoothed Signal")
plt.xlabel("Time (s)")
plt.ylabel("Amplitude")

plt.tight_layout()
plt.show()

write('sound/savitzky_golay_smoothed.wav', sampling_rate, savitzky_golay_smoothed.astype(np.float32))

from IPython.display import Markdown, Audio
import librosa.display

# Function to plot spectrogram with audio and label
def plot_spectrogram_with_audio(audio, sampling_rate, title="Spectrogram", audio_label="Audio Playback"):
    plt.figure(figsize=(10, 4))
    S = librosa.feature.melspectrogram(y=audio, sr=sampling_rate)
    S_dB = librosa.power_to_db(S, ref=np.max)
    librosa.display.specshow(S_dB, sr=sampling_rate, x_axis='time', y_axis='mel', cmap='magma')
    plt.colorbar(format='%+2.0f dB')
    plt.title(title)
    plt.tight_layout()
    
    # Display the audio label and audio player
    display(Markdown(f"**{audio_label}**"))
    display(Audio(audio, rate=sampling_rate))
    
    plt.show()

# Example: Plot original and filtered spectrograms with audio playback
plot_spectrogram_with_audio(audio, sampling_rate, title="Original Signal Spectrogram", audio_label="Original Audio Playback")
plot_spectrogram_with_audio(low_pass_1000, sampling_rate, title="Low-Pass Filtered (1kHz) Spectrogram", audio_label="Low-Pass 1000 Hz Audio Playback")
plot_spectrogram_with_audio(low_pass_2000, sampling_rate, title="Low-Pass Filtered (2kHz) Spectrogram", audio_label="Low-Pass 2000 Hz Audio Playback")
plot_spectrogram_with_audio(savitzky_golay_smoothed, sampling_rate, title="Savitzky-Golay Smoothed Spectrogram", audio_label="Savitzky-Golay Smoothed Audio Playback")

from IPython.display import display, Audio, Markdown
from scipy.signal import hilbert

# Function to plot envelope with audio and label
def plot_envelope_with_audio(audio, sampling_rate, title="Signal Envelope", audio_label="Audio Playback"):
    # Apply Hilbert transform to get the envelope
    analytic_signal = hilbert(audio)
    envelope = np.abs(analytic_signal)
    time = np.arange(len(envelope)) / sampling_rate

    # Plot the envelope
    plt.figure(figsize=(10, 4))
    plt.plot(time, envelope)
    plt.title(title)
    plt.xlabel('Time (s)')
    plt.ylabel('Envelope Amplitude')
    plt.tight_layout()
    plt.show()

    # Display a label for the audio
    display(Markdown(f"**{audio_label}**"))
    # Embed the audio playback directly below the envelope plot
    display(Audio(audio, rate=sampling_rate))

# Example: Plot envelopes and add labeled audio playback for original and filtered signals
plot_envelope_with_audio(audio, sampling_rate, title="Original Signal Envelope", audio_label="Original Audio")
plot_envelope_with_audio(low_pass_1000, sampling_rate, title="Low-Pass Filtered (1kHz) Envelope", audio_label="Low-Pass 1000 Hz Audio")
plot_envelope_with_audio(savitzky_golay_smoothed, sampling_rate, title="Savitzky-Golay Smoothed Signal Envelope", audio_label="Savitzky-Golay Smoothed Audio")

# Function to calculate KPIs and convert to standard Python floats
def calculate_kpis(signal, sampling_rate):
    # Convert signal to mono if necessary
    signal_mono = librosa.to_mono(signal)
    
    # Calculate standard KPIs
    mean = float(np.mean(signal_mono))
    std_dev = float(np.std(signal_mono))
    skewness = float(skew(signal_mono))
    dynamic_range = float(np.max(signal_mono) - np.min(signal_mono))
    entropy = float(shannon_entropy(signal_mono))
    
    # Spectral features
    spectral_centroid = float(np.mean(librosa.feature.spectral_centroid(y=signal_mono, sr=sampling_rate)))
    spectral_bandwidth = float(np.mean(librosa.feature.spectral_bandwidth(y=signal_mono, sr=sampling_rate)))
    zero_crossing_rate = float(np.mean(librosa.feature.zero_crossing_rate(signal_mono)))
    
    return {
        "Mean": mean,
        "Standard Deviation": std_dev,
        "Skewness": skewness,
        "Dynamic Range": dynamic_range,
        "Entropy": entropy,
        "Spectral Centroid": spectral_centroid,
        "Spectral Bandwidth": spectral_bandwidth,
        "Zero Crossing Rate": zero_crossing_rate
    }

# Example: Calculate KPIs for original and processed signals
kpis_original = calculate_kpis(audio, sampling_rate)
kpis_shifted_3000 = calculate_kpis(shifted_3000_up, sampling_rate)
kpis_low_pass_1000 = calculate_kpis(low_pass_1000, sampling_rate)


# Prepare the KPI data
kpis_data = {
    "Original Signal": kpis_original,
    "+3000 Hz Shifted": kpis_shifted_3000,
    "Low-Pass 1000 Hz": kpis_low_pass_1000
}

# Convert the KPI data into a DataFrame for a cleaner table display
kpis_df = pd.DataFrame(kpis_data)

# Display the KPIs in table format
print(kpis_df)

                    Original Signal  +3000 Hz Shifted  Low-Pass 1000 Hz
Mean                      -0.000004          0.000003         -0.000004
Standard Deviation         0.109932          0.074204          0.014490
Skewness                   0.039047          0.038837         -0.079182
Dynamic Range              1.947723          1.586893          0.711466
Entropy                   13.321276         16.885730         16.886721
Spectral Centroid       2256.571799       2672.534128        944.300994
Spectral Bandwidth      1413.563726       1437.703945        260.682652
Zero Crossing Rate         0.161120          0.195181          0.085631

import matplotlib.pyplot as plt
import numpy as np

# Function to create bar charts for comparing KPIs
def plot_kpi_comparison(kpis_dict, title):
    """
    kpis_dict: Dictionary of KPIs with keys as signal names and values as KPI dictionaries.
    """
    metrics = list(kpis_dict[list(kpis_dict.keys())[0]].keys())  # Get list of KPI names
    
    # Create a figure for the bar charts
    num_metrics = len(metrics)
    fig, axs = plt.subplots(1, num_metrics, figsize=(20, 5))
    
    signals = list(kpis_dict.keys())  # List of signals (Original, Shifted, Filtered)
    
    for i, metric in enumerate(metrics):
        values = [kpis_dict[signal][metric] for signal in kpis_dict]
        axs[i].bar(signals, values)
        axs[i].set_title(metric)
        axs[i].set_ylabel('Value')
        axs[i].set_xticks(range(len(signals)))  # Set the tick positions
        axs[i].set_xticklabels(signals, rotation=45)  # Set tick labels after setting the positions
    
    fig.suptitle(title)
    plt.tight_layout()
    plt.show()

# Example: Prepare KPI data for visualization
kpis_data = {
    "Original Signal": kpis_original,
    "+3000 Hz Shifted": kpis_shifted_3000,
    "Low-Pass 1000 Hz": kpis_low_pass_1000
}

# Plot KPI comparisons
plot_kpi_comparison(kpis_data, title="KPI Comparison for Original and Processed Signals")

Challenge 1¶

1. Basics of Image and Signal Processing (LE1)¶

1.1. Image Properties¶

Day 1¶

Day 2¶

Problem:¶

Use Case:¶

Objectives of the Experiments:¶

Explanation, Why are these adjustments relevant?¶

Day 3¶

Reasoning for Methods and Parameters:¶

Observations:¶

Day 4¶

Hue shifts 0, 40, 80 and 120¶

Hue shifts 20, 60, 100 and 140¶

Reasoning for Methods and Parameters:¶

Observations:¶

GIF¶

Day 5¶

Gamma (0.55)¶

Linear Brightness (-60)¶

Day 6¶

Day 7¶

Day 8¶

Day 9¶

Day 10¶

Day 11¶